1. Linear Models (II) Rong Jin

2. Recap  Classification problems Inputs x  output y y is from a discrete set Example: height 1.8m  male/female?  Statistical learning approaches for classification problems Training ExamplesLearning a Statistical Model  Prediction p(y|x;  ) (1.8m, m) (1.87, m) (1.65, f) (1.66, m) (1.58, f) (1.63, f) p(h|male), p(male) p(f|male), p(female) p(male|1.8) p(female|1.8)

3. Recap  Generative Model p(y|x): determine the class y for object x  p(y): how frequent class y appears  p(x|y): the input pattern for class y  Example: 1.8m  male? female? p(male|1.8m) = p(male)p(1.8m|male)/p(1.8m) p(female|1.8m) = p(female)p(1.8m|female)/p(1.8m) p(1.8m) = p(1.8m|male)p(male)+p(1.8m|female)p(female)

4. Recap  Learning p(x|y) and p(y) p(y) = #example(y)/#examples Maximum likelihood estimation for p(x|y)  Example Training examples:  (1.8m, m) (1.87, m) (1.65, f) (1.66, m) (1.58, f) (1.63, f) p(male) = N male /N p(female) = N female /N Assume that the height distributions for male and female are Gaussian  (  male,  male ), (  female,  female ) MLE estimation

5. Recap

7.  Naïve Bayes Input x is a vector: x={x 1, x 2,…,x m } Assume each feature is independent from each other given the class y  p(x|y)=p(x 1 |y)p(x 2 |y)…p(x m |y)  each p(x i |y) is estimated using MLE approach

8. Text Classification (I)  Learning to classify text Input x: document  Represented by a vector of words Output y: interesting or not  +1 for interesting document, -1 for uninteresting  Generative model for text classification (TC) p(+), p(-) p(doc|+), p(doc|-) Naïve Bayes approach

9. Text Classification (II)  Learning parameters for TC p(+) = n(+)/N, p(-) = n(-)/N  n(  ): number of positive (or negative) documents  N: total number of documents Apply MLE for estimating p(w|+), p(w|-)

10. Text Classification (IV) Twenty NewsgroupsAn Example

11. Text Classification (IV)  Any problems with the naïve Bayes text classifier?

12. Text Classifier (V)  Problems Irrelevant words Unseen words  Solution Select relevant words using mutual information I(x, y)  x: whether or not word x appearing in a document  y: the document is of interests or not Unseen words  Word class approach Introduce word class T= {t 1, t 2, …, t m } Compute p(t i |+), p(t i |-) When w is unseen before, replace p(w|  ) with p(t i |  )  Word correlation approach finding out the correlations between words p(w|w’)  Using web information p(w|  ) =  w’ p(w|w’)p(w’|  )

13. Logistic Regression Model  Gaussian generative model == find a linear decision boundary.  Why not learn a linear decision boundary directly?

14. Logistic Regression Model  The log-ratio of positive class to negative class  Results

15. Logistic Regression Model  Assume the inputs and outputs are related in the log linear function  Estimate weights: MLE approach

16. Example 1: Heart Disease Input feature x: age group id output y: having heart disease or not +1: having heart disease -1: no heart disease 1: 25-29 2: 30-34 3: 35-39 4: 40-44 5: 45-49 6: 50-54 7: 55-59 8: 60-64

17. Example 1: Heart Disease Logistic regression model Learning w and c: MLE approach Numerical optimization: w = 0.58, c = -3.34

18. Example 1: Heart Disease  W = 0.58 An old person is more likely to have heart disease  C = -3.34 i  w+c < 0  p(+|i) < p(-|i) i  w+c > 0  p(+|i) > p(-|i) i  w+c = 0  decision boundary  i* = 5.78  53 year old

19. Naïve Bayes Solution Inaccurate fitting Non Gaussian distribution i* = 5.59 Close to the estimation by logistic regression Even though naïve Bayes does not fit input patterns well, it still works fine for the decision boundary

20. Problems with Using Histogram Data?

21. Uneven Sampling for Different Ages

22. Solution w = 0.63, c = -3.56  i* = 5.65

23. Example: Text Classification  Input x: a binary vector Each word is a different dimension x i = 0 if the ith word does not appear in the document x i = 1 if it appears in the document  Output y: interesting document or not +1: interesting -1: uninteresting

24. Example: Text Classification Doc 1 The purpose of the Lady Bird Johnson Wildflower Center is to educate people around the world, … Doc 2 Rain Bird is one of the leading irrigation manufacturers in the world, providing complete irrigation solutions for people… termtheworldpeoplecompanycenter… Doc 111101… Doc 211110…

25. Example 2: Text Classification  Logistic regression model Every term t i is assigned with a weight w i  Learning parameters: MLE approach  Need numerical solutions

26. Example 2: Text Classification  Weight w i w i > 0: term t i is a positive evidence w i < 0: term t i is a negative evidence w i = 0: term t i is irrelevant to whether the document is intesting The larger the | w i |, the more important t i term is determining whether the document is interesting.  Threshold c

27. Example 2: Text Classification Dataset: Reuter-21578 Classification accuracy Naïve Bayes: 77% Logistic regression: 88%

28. Why Logistic Regression Works better for Text Classification?  Common words Small weights in logistic regression Large weights in naïve Bayes  Weight ~ p(w|+) – p(w|-)  Independence assumption Naive Bayes assumes that each word is generated independently Logistic regression is able to take into account of the correlation of words

29. Comparison Generative Model Model P(x|y) Model the input patterns Usually fast converge Cheap computation Robust to noise data But Usually performs worse Discriminative Model Model P(y|x) directly Model the decision boundary Usually good performance But Slow convergence Expensive computation Sensitive to noise data